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Social Media and Social Computing

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  1. Social Media and Social Computing Chapter 1 Chapter 1, Community Detection and Mining in Social Media.  Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. 

  2. Traditional Media Broadcast Media: One-to-Many Communication Media: One-to-One

  3. Social Media: Many-to-Many

  4. Characteristics of Social Media • “Consumers” become “Producers” • Rich User Interaction • User-Generated Contents • Collaborative environment • Collective Wisdom • Long Tail Broadcast Media Filter, then Publish Social Media Publish, then Filter

  5. Top 20 Websites at USA 40% of websites are social media sites

  6. Networks and Representation Social Network: A social structure made of nodes (individuals or organizations) and edges that connect nodes in various relationships like friendship, kinship etc. • Graph Representation • Matrix Representation

  7. Basic Concepts • A: the adjacency matrix • V: the set of nodes • E: the set of edges • vi: a node vi • e(vi, vj): an edge between node vi and vj • Ni: the neighborhood of node vi • di: the degree of node vi • geodesic: a shortest path between two nodes • geodesic distance

  8. Properties of Large-Scale Networks • Networks in social media are typically huge, involving millions of actors and connections. • Large-scale networks in real world demonstrate similar patterns • Scale-free distributions • Small-world effect • Strong Community Structure

  9. Scale-free Distributions • Degree distribution in large-scale networks often follows a power law. • A.k.a. long tail distribution, scale-free distribution

  10. log-log plot • Power law distribution becomes a straight line if plot in a log-log scale Friendship Network in Flickr Friendship Network in YouTube

  11. Small-World Effect • “Six Degrees of Separation” • A famous experiment conducted by Travers and Milgram (1969) • Subjects were asked to send a chain letter to his acquaintance in order to reach a target person • The average path length is around 5.5 • Verified on a planetary-scale IM network of 180 million users (Leskovec and Horvitz 2008) • The average path length is 6.6

  12. Diameter • Measures used to calibrate the small world effect • Diameter: the longest shortest path in a network • Average shortest path length • The shortest path between two nodes is called geodesic. • The number of hops in the geodesic is the geodesic distance. • The geodesic distance between node 1 and node 9 is 4. • The diameter of the network is 5, corresponding to the geodesic distance between nodes 2 and 9.

  13. Community Structure • Community: People in a group interact with each other more frequently than those outside the group • Friends of a friend are likely to be friends as well • Measured by clustering coefficient: • density of connections among one’s friends

  14. Clustering Coefficient • d6=4, N6= {4, 5, 7,8} • k6=4 as e(4,5), e(5,7), e(5,8), e(7,8) • C6 = 4/(4*3/2) = 2/3 • Average clustering coefficient C = (C1 + C2 + … + Cn)/n • C = 0.61 for the left network • In a random graph, the expected coefficient is 14/(9*8/2) = 0.19.

  15. Challenges • Scalability • Social networks are often in a scale of millions of nodes and connections • Traditional Network Analysis often deals with at most hundreds of subjects • Heterogeneity • Various types of entities and interactions are involved • Evolution • Timeliness is emphasized in social media • Collective Intelligence • How to utilize wisdom of crowds in forms of tags, wikis, reviews • Evaluation • Lack of ground truth, and complete information due to privacy

  16. Social Computing Tasks • Social Computing: a young and vibrant field • Many new challenges • Tasks • Network Modeling • Centrality Analysis and Influence Modeling • Community Detection • Classification and Recommendation • Privacy, Spam and Security

  17. Network Modeling • Large Networks demonstrate statistical patterns: • Small-world effect (e.g., 6 degrees of separation) • Power-law distribution (a.k.a. scale-free distribution) • Community structure (high clustering coefficient) • Model the network dynamics • Find a mechanism such that the statistical patterns observed in large-scale networks can be reproduced. • Examples: random graph, preferential attachment process, Watts and Strogatz model • Used for simulation to understand network properties • Thomas Shelling’s famous simulation: What could cause the segregation of white and black people • Network robustness under attack

  18. Comparing Network Models observations over various real-word large-scale networks outcome of a network model (Figures borrowed from “Emergence of Scaling in Random Networks”)

  19. Centrality Analysis and Influence Modeling • Centrality Analysis: • Identify the most important actors or edges • Various criteria • Influence modeling: • How is information diffused? • How does one influence each other? • Related Problems • Viral marketing: word-of-mouth effect • Influence maximization

  20. Community Detection • A community is a set of nodes between which the interactions are (relatively) frequent • A.k.a., group, cluster, cohesive subgroups, modules • Applications: Recommendation based communities, Network Compression, Visualization of a huge network • New lines of research in social media • Community Detection in Heterogeneous Networks • Community Evolution in Dynamic Networks • Scalable Community Detection in Large-Scale Networks

  21. Classification and Recommendation • Common in social media applications • Tag suggestion, Friend/Group Recommendation, Targeting Link prediction Network-Based Classification

  22. Privacy, Spam and Security • Privacy is a big concern in social media • Facebook, Google buzz often appear in debates about privacy • NetFlix Prize Sequel cancelled due to privacy concern • Simple annoymization does not necessarily protect privacy • Spam blog (splog), spam comments, Fake identity, etc., all requires new techniques • As private information is involved, a secure and trustable system is critical • Need to achieve a balance between sharing and privacy

  23. Book Available at Morgan & claypool Publishers Amazon If you have any comments,please feel free to contact: • Lei Tang, Yahoo! Labs, ltang@yahoo-inc.com • Huan Liu, ASU huanliu@asu.edu